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云边智能: 电力系统运行控制的边缘计算方法及其应用现状与展望

白昱阳 黄彦浩 陈思远 张俊 李柏青 王飞跃

白昱阳, 黄彦浩, 陈思远, 张俊, 李柏青, 王飞跃. 云边智能: 电力系统运行控制的边缘计算方法及其应用现状与展望. 自动化学报, 2020, 46(3): 397−410 doi: 10.16383/j.aas.2020.y000001
引用本文: 白昱阳, 黄彦浩, 陈思远, 张俊, 李柏青, 王飞跃. 云边智能: 电力系统运行控制的边缘计算方法及其应用现状与展望. 自动化学报, 2020, 46(3): 397−410 doi: 10.16383/j.aas.2020.y000001
Bai Yu-Yang, Huang Yan-Hao, Chen Si-Yuan, Zhang Jun, Li Bai-Qing, Wang Fei-Yue. Cloud-edge intelligence: status quo and future prospective of edge computing approaches and applications in power system operation and control. Acta Automatica Sinica, 2020, 46(3): 397−410 doi: 10.16383/j.aas.2020.y000001
Citation: Bai Yu-Yang, Huang Yan-Hao, Chen Si-Yuan, Zhang Jun, Li Bai-Qing, Wang Fei-Yue. Cloud-edge intelligence: status quo and future prospective of edge computing approaches and applications in power system operation and control. Acta Automatica Sinica, 2020, 46(3): 397−410 doi: 10.16383/j.aas.2020.y000001

云边智能: 电力系统运行控制的边缘计算方法及其应用现状与展望

doi: 10.16383/j.aas.2020.y000001
基金项目: 国家电网公司科技项目(XT71-19-032)资助
详细信息
    作者简介:

    白昱阳:武汉大学电气与自动化学院硕士研究生. 2019 年获得武汉大学电气工程学院学士学位. 主要研究方向为边缘计算在电力系统运行和控制中的应用. E-mail: baiyuyang@whu.edu.cn

    黄彦浩:中国电力科学研究院有限公司博士, 高级工程师. 主要研究方向为电力系统仿真分析. E-mail: hyhao@epri.sgcc.com.cn

    陈思远:武汉大学电气与自动化学院博士研究生. 2018 年获得武汉大学电气工程学院硕士学位. 主要研究方向为边缘计算在电力系统运行和控制中的应用. E-mail: wddqcsy@whu.edu.cn

    张俊:武汉大学电气与自动化学院教授. 2003 年和 2005 年分别获得华中科技大学电子信息与通信工程系学士与硕士学位. 2008 年获得亚利桑那州立大学电气工程博士学位. 主要研究方向为智能系统, 人工智能, 知识自动化, 及其在智能电力和能源系统中的应用. 本文通信作者. E-mail: jun.zhang@qaii.ac.cn

    李柏青:中国电力科学研究院有限公司教授级高级工程师, 主要研究方向为电力系统分析与运行控制技术.E-mail: libq@epri.sgcc.com.cn

    王飞跃:中国科学院自动化研究所复杂系统管理与控制国家重点实验室主任, 国防科技大学军事计算实验与平行系统技术研究中心主任, 中国科学院大学中国经济与社会安全研究中心主任, 青岛智能产业技术研究院院长. 主要研究方向为平行系统的方法与应用, 社会计算, 平行智能以及知识自动化. E-mail: feiyue.wang@ia.ac.cn

Cloud-edge Intelligence: Status Quo and Future Prospective of Edge Computing Approaches and Applications in Power System Operation and Control

Funds: Supported by Science and Technology Project of State Grid Corporation of China (XT71-19-032)
  • 摘要: 本文分析了当前我国电力系统的运行与控制面临的挑战, 对边缘计算的发展背景和关键技术进行了介绍, 阐述了云边协同和边边协同的功能与特征, 并对边缘协同技术下的边缘智能技术进行了探讨. 结合电力系统的层级式构架, 讨论了在电网部署边缘计算层的方法, 提出利用云边协同、边边协同、边缘智能等技术解决电力系统面临的实时性高、数据周期短、任务复杂等难题, 在减轻边缘节点与云中心通信压力的同时, 提高业务服务质量, 保障边缘节点的数据隐私. 通过对边缘计算在“源 − 网 − 荷”各环节的应用前景进行分析与讨论, 阐述了边缘计算在电网中的可行性与实用性. 最后, 对边缘计算的应用范式与方案进行了总结, 并对其在未来电力系统中的发展方向进行了展望.
  • 图  1  边缘智能架构

    Fig.  1  The architecture of edge intelligence

    图  2  云边协同和边边联邦协同的联合训练框架

    Fig.  2  Joint training framework for cloud-edge collaboration and edge-edge federation collaboration

    图  3  边缘节点在电力系统中的部署示意图

    Fig.  3  Schematic diagram of edge node deployment in power system

    图  4  边缘计算平台在电力系统中的应用框架

    Fig.  4  Application framework of edge computing platform in power system

    图  5  变电站综自系统结构图

    Fig.  5  Structure of integrated automation system of substation

    图  6  边缘计算在变电站中的数据交互示意图

    Fig.  6  Data interaction diagram of edge computing in substation

    图  7  负荷建模边缘计算构架图

    Fig.  7  The architecture diagram of edge computing for load modelling

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  • 收稿日期:  2019-11-08
  • 录用日期:  2019-12-01
  • 网络出版日期:  2020-03-30
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